Computer techniques for machine recognition of hand printed characters have advanced considerably in the recent past. As a margin between error rates of a particular character recognition procedure and those of humans continues to decrease, further reductions in the error rate have become progressively more difficult to attain. In the case of neural-network-based algorithms, for example, this difficulty translates to procuring ever larger sets of training data for progressively smaller gains in error rates.
In view of this phenomena, one technique for reducing this error rate for machine based recognition processes has been to simultaneously use multiple recognition procedures, that have uncorrelated errors (i.e. make different kinds of mistakes), so that overall performance is enhanced. This technique exacts a fairly severe computational penalty, since the different procedures often have completely different (and hence non-shareable) intermediate steps, all of which have to be performed for each character.
A more efficient technique has been to simultaneously use only one or a few independent procedures for most characters and use additional independent procedures only for characters having a low measure of confidence. Although this technique can improve recognition error rates, the associated computational penalty can still be considerable. Unfortunately, this technique can be used effectively only with procedures that can provide a reasonably accurate measure of confidence.
More specifically, conventional optical character recognition (OCR) systems generally have as many outputs as there are characters to be recognized. An input character is usually identified by examining the values of the outputs. One of the simplest methods is to choose a character for the input character being recognized as that which corresponds to a particular output that has a maximum (or minimum) value. The confidence measure is also a function of the output values. In its most basic form, the value of the maximum (or minimum) output may be a measure of the confidence. A slightly more sophisticated confidence measure can be determined as a ratio between the maximum and a next highest output. However, the accuracy of confidence measure varies widely between the methods of measurement and between different OCR procedures.
Thus, a need still exists in the art for increasingly efficient techniques of reducing the margin between the error rates of particular OCR procedures and those of humans.